Fluorescent in situ hybridization (FISH) imaging has been proved as a powerful molecular imaging tool to detect cancers, predict cancer prognosis, and monitor therapy efficacy. However, manual FISH analytic method is very tedious and introducing large inter-reader variability. It is difficult or unpractical to use manual FISH analytic method to detect small number of abnormal chromosome cells from the overwhelming normal cells depicted on one specimen (i.e., screening for early cervical cancer using Pap smear). To solve this difficulty, we proposed to develop and test a prototype system of automated FISH imaging analysis. The system includes both an optical imager and a computer-aided detection (CAD) scheme. Comparing to the currently available techniques, our proposed system provides unique advantages. First, it improves imaging efficiency. Second, it allows CAD processing to detect significantly more analyzable or abnormal cells from a testing specimen than the current manual FISH image analytic method. Our hypothesis is that if all cells and FISH signals depicted on one specimen can be automatically imaged and correctly counted, the probability of missing early cancer sign of abnormal or carcinoma cells can be reduced and thus improving sensitivity of cancer diagnosis. To test this hypothesis, we will conduct a number of specific research tasks. First, we will collect and assemble a preliminary image database that includes both cytology and FISH images for each Pap smear examination. Approximately half of cases depict cervical abnormalities that lead to the development of cancer. Second, we will build a prototype fluorescent image scanning system and test its quality and reliability. Third, we will also develop and preliminarily test the accuracy of our CAD schemes in selecting and segmenting analyzable interphase cells from clinical images, identifying and counting independent FISH labeled biomarker signals (spots), and classifying between the normal and abnormal (cancer) cases. After initial integration of our preliminary imaging system and CAD scheme, we will conduct characterization study to test the performance of the technique. This pilot study will help us to preliminarily assess the feasibility of developing and applying this automated FISH image analysis system and method. The success of this project can generate more follow-up clinical studies that are unable to do in the past.